Search Results (13)

Electoral fraud, particularly multiple voting, undermines the integrity of democratic processes. To address this challenge, this study introduces an innovative facial recognition system that integrates an enhanced 50-layer Residual Network (ResNet50) architecture with Additive Angular Margin Loss (ArcFace) and Multi-Task Cascaded Convolutional Neural Networks (MTCNN) for face detection. Using the Mahalanobis distance, the system verifies voter identities by comparing captured facial images with previously recorded biometric features. Extensive evaluations demonstrate the methodology’s effectiveness, achieving a facial recognition accuracy of 99.85%. This significant improvement over existing baseline methods has the potential to enhance electoral transparency and prevent multiple voting. The findings contribute to developing robust biometric-based electoral systems, thereby promoting democratic trust and accountability. Full article

Gas chromatography–ion mobility spectrometry (GC-IMS) is an interesting candidate to face geographical origin declaration fraud in dehydrated apple samples. It allows the collection of the peculiar fingerprints of the analysed samples with the bi-dimensional separation of volatile molecules, based on their polarity and their dimension and shape. It represents a rapid, cost-effective, and sensitive solution for food authenticity issues. A design of experiment (DoE) led to robust sampling, taking into account different factors, such as harvesting year, the presence of peel, variety. The sample preparation was limited as it required only the milling of the dehydrated apple dices before the analysis. The GC-IMS analytical method permitted us to obtain of a 3D graph in 11 min, and the multivariate statistical analysis returned a clear separation between Italian and non-Italian (French, Chinese, Hungarian, Polish) samples, considering both unsupervised and supervised approaches. The statistical model, created employing a training set, was applied on a further test set, with a good overall performance. Thus, GC-IMS could play a relevant role as a tool to prevent/fight false origin declaration frauds and also, potentially, other kinds of food authenticity and safety frauds. Full article

One of the fundamental components of internal controls, a whistleblowing system (WBS) is crucial for preventing fraud, addressing irregularities, and enhancing good governance. The purpose of this study is to investigate the impact of whistleblower disclosures on earnings management in Saudi Arabia’s Takaful Insurance (TKI) sector between 2017 and 2023. To this end, a whistleblowing index was constructed as a tool to evaluate the whistleblowing framework’s effectiveness. Using the Dynamic Generalized Method of Moments (GMM) to account for endogeneity, it was found that most Saudi insurance companies increased their efforts to disclose information about whistleblowers, which significantly reduced earnings management practices. Specifically, the study concludes that the size of the audit committee (ACS) significantly and negatively affects how insurance businesses manage their earnings when a whistleblower system is in place. Additionally, there is a notable and adverse effect on earnings management from board size (BSZ), the percentage of non-executive independent members (PNIM), and Shariah board size (SBS). However, it was found that earnings management is unaffected by the frequency of board meetings (BMFR). This study adds to the body of knowledge by demonstrating how corporate governance enhances the effectiveness of the whistleblowing system. Full article

The primary concern of research in the area of fraud risk relevant to sustainability reporting lies in understanding the potential for fraudulent or misleading reporting practices and developing strategies and tools to identify and prevent such behaviors. Taking into consideration that significant research has yet to be conducted on fraud risk models associated with sustainability reporting, this study represents an innovative contribution. It uses a mixed-methods approach to design a fraud risk model based on sustainability reporting. Given its type and approach, this research does not posit any hypotheses involving thematic analysis and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method. This study is exploratory and applied, aiming to design a model through a mixed-methods methodology. Therefore, the research is hypothesis-free and instead utilizes a qualitative sample of experts and academics in the field of accounting from Iran and Denmark. The DEMATEL technique identifies key external and internal factors that significantly impact sustainability reporting, including comprehensive internal controls, strong governance and oversight, training and awareness, the utilization of technology, and data analytics. The influence of stakeholders, third-party audits, and the credibility of sustainability reports emerges as particularly significant in this context, exceeding the impact of other factors. These findings underscore that stakeholders, third-party audits, and report credibility to play a more prominent role in shaping sustainability performance compared to other considerations. This would imply that such variables remain key drivers in the perceptiveness and effectiveness of sustainability performance. Full article

The complexity and interconnection within the financial ecosystem demand innovative solutions to improve transparency, security, and efficiency in financial reporting and liquidity management, while also reducing accounting fraud. This paper presents Blockchain Financial Statements (BFS), an innovative accounting system designed to address accounting fraud, reduce data manipulation, and misrepresentation of company financial claims, by enhancing availability of the real-time and tamper-proof accounting data, underpinned by a verifiable approach to financial transactions and reporting. The primary goal of this research is to design, develop, and validate a blockchain-based accounting prototype—the BFS system—that can automate transformation of transactional data, generated by traditional business activity into comprehensive financial statements. Incorporating a Design Science Research Methodology with Domain-Driven Design, this study constructs a BFS artefact that harmonises accounting standards with blockchain technology and business orchestration. The resulting Java implementation of the BFS system demonstrates successful integration of blockchain technology into accounting practices, showing potential in real-time validation of transactions, immutable record-keeping, and enhancement of transparency and efficiency of financial reporting. The BFS framework and implementation signify an advancement in the application of blockchain technology in accounting. It offers a functional solution that enhances transparency, accuracy, and efficiency of financial transactions between banks and businesses. This research underlines the necessity for further exploration into blockchain’s potential within accounting systems, suggesting a promising direction for future innovations in tamper-evident financial reporting and liquidity management. Full article

This study examines the impact of executives implicated in fraud on firms’ investment decisions using publicly disclosed Accounting and Auditing Enforcement Releases (AAERs) of the U.S. Securities and Exchange Commission (SEC), aiming to address the underexplored aspect of rationalization within the fraud triangle. AAERs summarize enforcement actions subject to civil lawsuits brought by the SEC in federal court. Executives implicated in fraud often display abnormal attitudes to justify accounting irregularities, prompting an investigation into how abnormal investment decisions are used for rationalizing fraud, given their critical role in a firm’s long-term sustainability. We utilize bootstrap analysis to address the non-normality of fraud firms in our sample, and to acquire multiple bootstrap samples that represent the fraud population, thereby bolstering the reliability of our statistical analysis. Analysis of AAERs spanning from 1981 to 2013 reveals that implicated executives, particularly CEOs and CFOs, tend to make abnormal investment decisions, and that collusive fraud exacerbates this behavior. Notably, such executives lean towards overinvestment, particularly in R&D expenditure, to hide or justify fraud; the duration of fraud amplifies its impact on investment decisions. By shedding light on the rationalization aspect of the fraud triangle, this research contributes valuable insights for investors, regulators, and academia, emphasizing the significance of public disclosure of fraud by regulators to enhance transparency in capital markets and to alert capital market participants. Furthermore, this study underscores the importance of ethics-focused education in accounting to prevent corporate fraud. Full article

Major financial irregularities have contributed significantly to the destabilization of the world economy and the financial environment, by short circuiting investment flows and discrediting financial markets, with significant financial, social, and political consequences. Through the auditor’s key role of providing an independent, objective and professional opinion on the correctness of financial statements, the accounting profession has promoted a new procedure, the anti-fraud audit, which is responsible solely for financial prevention and fraud detection. Fraud detection audits have a methodology and a set of customized tools that help auditors in their mission to ensure the smooth execution of their audits. The purpose of this research is to conduct a comprehensive examination of both theoretical and practical aspects, with the objective of determining the risk profile of financial fraud among auditors. This will aid in preventing, detecting, and correcting such harmful practices. Through an empirical study of a fraudulent corporate entity, the quality of information contained within financial reports will be assessed, as well as the effectiveness of managerial decision-making substantiation. The data processing was carried out using the statistical software SPSS 19.0. when making graphs and interpreting the obtained results. Full article

In the construction of a telecom-fraud risk warning and intervention-effect prediction model, how to apply multivariate heterogeneous data to the front-end prevention and management of telecommunication network fraud has become one of the focuses of this research. The Bayesian network-based fraud risk warning and intervention model was designed by taking into account existing data accumulation, the related literature, and expert knowledge. The initial structure of the model was improved by utilizing City S as an application example, and a telecom-fraud analysis and warning framework was proposed by incorporating telecom-fraud mapping. After the evaluation in this paper, the model shows that age has a maximum sensitivity of 13.5% to telecom-fraud losses; anti-fraud propaganda can reduce the probability of losses above 300,000 yuan by 2%; and the overall telecom-fraud losses show that more occur in the summer and less occur in the autumn, and that the Double 11 period and other special time points are prominent. The model in this paper has good application value in the real-world field, and the analysis of the early warning framework can provide decision support for the police and the community to identify the groups, locations, and spatial and temporal environments prone to fraud, to combat propaganda and provide a timely warning to stop losses. Full article

Currently, fraud detection is employed in numerous domains, including banking, finance, insurance, government organizations, law enforcement, and so on. The amount of fraud attempts has recently grown significantly, making fraud detection critical when it comes to protecting your personal information or sensitive data. There are several forms of fraud issues, such as stolen credit cards, forged checks, deceptive accounting practices, card-not-present fraud (CNP), and so on. This article introduces the credit card-not-present fraud detection and prevention (CCFDP) method for dealing with CNP fraud utilizing big data analytics. In order to deal with suspicious behavior, the proposed CCFDP includes two steps: the fraud detection Process (FDP) and the fraud prevention process (FPP). The FDP examines the system to detect harmful behavior, after which the FPP assists in preventing malicious activity. Five cutting-edge methods are used in the FDP step: random undersampling (RU), t-distributed stochastic neighbor embedding (t-SNE), principal component analysis (PCA), singular value decomposition (SVD), and logistic regression learning (LRL). For conducting experiments, the FDP needs to balance the dataset. In order to overcome this issue, Random Undersampling is used. Furthermore, in order to better data presentation, FDP must lower the dimensionality characteristics. This procedure employs the t-SNE, PCA, and SVD algorithms, resulting in a speedier data training process and improved accuracy. The logistic regression learning (LRL) model is used by the FPP to evaluate the success and failure probability of CNP fraud. Python is used to implement the suggested CCFDP mechanism. We validate the efficacy of the hypothesized CCFDP mechanism based on the testing results. Full article

To ensure the economic security of companies, it is necessary to introduce a risk management system based on the use of various tools, especially financial ones. The purpose of the article is to scientifically substantiate the paradigm of integration of the risk management mechanism into the system of economic security in companies on the basis of risk-oriented management. The main study method was an online survey of 50 Polish companies in January–April 2021 using a developed questionnaire consisting of 40 questions. According to the results of the expert survey, it is determined that regardless of the type of economic activity of the enterprise, the main goal of introducing risk-oriented management is to preserve assets and increase the efficiency of financial and economic processes. The introduction of risk-oriented management is perceived as a tool to increase the value of the company and ensure the achievement of strategic goals. Fraud is a significant risk to the state of economic security for modern enterprises. To prevent the fact of fraud, taking into account the specifics of the operation of companies, it is suggested to conduct an annual examination. As a result, the suggested procedure should include an audit (audit of financial statements, forensics, transition to international financial reporting standards, audit of systems and processes), assessment (assessment for audit and reporting in accordance with international financial reporting standards, risk management assessment in accordance with international standards, assessment of the effectiveness of economic security), tax analytics (identification of tax risks, analysis of compliance with tax legislation, tax audit), and a due diligence procedure for investment objects. Full article

Governments need to be accountable and transparent for their public spending decisions in order to prevent losses through fraud and corruption as well as to build healthy and sustainable economies. Open data act as a major instrument in this respect by enabling public administrations, service providers, data journalists, transparency activists, and regular citizens to identify fraud or uncompetitive markets through connecting related, heterogeneous, and originally unconnected data sources. To this end, in this article, we present our experience in the case of Slovenia, where we successfully applied a number of anomaly detection techniques over a set of open disparate data sets integrated into a Knowledge Graph, including procurement, company, and spending data, through a linked data-based platform called TheyBuyForYou. We then report a set of guidelines for publishing high quality procurement data for better procurement analytics, since our experience has shown us that there are significant shortcomings in the quality of data being published. This article contributes to enhanced policy making by guiding public administrations at local, regional, and national levels on how to improve the way they publish and use procurement-related data; developing technologies and solutions that buyers in the public and private sectors can use and adapt to become more transparent, make markets more competitive, and reduce waste and fraud; and providing a Knowledge Graph, which is a data resource that is designed to facilitate integration across multiple data silos by showing how it adds context and domain knowledge to machine-learning-based procurement analytics. Full article

The EU General Food Law not only aims at ensuring food safety but also to ‘prevent fraudulent or deceptive practices; the adulteration of food; and any other practices which may mislead the consumer’. Especially the partial or complete, deliberate, and intentional substitution of valuable ingredients (e.g., Saffron) for less valuable ones is of concern. Due to the variety of products on the market an approach to detect food adulteration that works well for one species may not be easily applicable to another. Here we present a broadly applicable approach for the detection of substitution of biological materials based on digital PCR. By simultaneously measuring and forecasting the number of genome copies in a sample, fraud is detectable as a discrepancy between these two values. Apart from the choice of target gene, the procedure is identical across all species. It is scalable, rapid, and has a high dynamic range. We provide proof of concept by presenting the analysis of 141 samples of Saffron (Crocus sativus) from across the European market by DNA accounting and the verification of these results by NGS analysis. Full article

Tea (Camellia sinensis) is one of the most popular beverages, commonly consumed all over the world. Depending on the fermentation process, tea leaves can be categorized into three major groups: unfermented green tea, semifermented Oolong tea, and fully fermented black tea. The latter accounts for over 80% of worldwide production. The quality of tea products is determined by color, freshness, strength, and aroma. Phenolic and polyphenolic components contribute to the color and taste, whereas volatile components are directly related to the aroma. Unfortunately, food fraud is increasing globally. The widespread adulteration is the main concern for commercial functional tea extracts and tea-based nutraceuticals on the market. Especially for powdered extracts, the product quality of functional tea extracts varies highly on the market. The growing demand and interest in functional tea extracts are causing the proliferation of frauds that can seriously affect public health. Chicory, husk of pulses, and cereal starch are non-permitted materials typically employed as adulterants in tea extracts. The aim of this work was to develop an efficient untargeted high-performance liquid chromatography with ultraviolet and fluorescence detection (HPLC-UV-FLD) method in combination with chemometrics to address the characterization, classification, and authentication of tea samples, together with possible adulterants such as chicory extracts. A reversed-phase chromatographic separation was optimized, using a C18 column, and 0.1% formic acid aqueous solution and acetonitrile as the mobile phase components. The proposed methodology was applied to 87 tea samples, differing in variety and production region, and 12 chicory samples. In any case, the sample treatment consisted of sample infusion with hot water and filtration, and the obtained HPLC-UV-FLD fingerprints were subjected to principal component analysis (PCA) and partial least squares regression-discriminant analysis (PLS-DA) chemometric methods. Perfect discrimination was achieved between different tea varieties and chicory demonstrating that untargeted HPLC-UV-FLD fingerprints can be proposed as good sample chemical descriptors to assess tea authentication and to prevent frauds dealing with adulteration with chicory. The poster of this work is provided in the supplementary materials. Full article